农林牧区两种桉树杂交种木材量量化的体积模型

Q3 Agricultural and Biological Sciences Australian Journal of Crop Science Pub Date : 2023-04-01 DOI:10.21475/ajcs.23.17.04.p3679
Wander Luis Barbosa Borges, Rogério Soares de Freitas, Giane Serafim da Silva, Marcela Aparecida de Moraes Silvestre, Mario Luiz Teixeira de Moraes
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引用次数: 0

摘要

林分木材量的量化对森林可持续管理的规划至关重要。量化木材体积最有效的方法是体积方程。许多调整体积方程的模型已经开发和测试,但是它们对农林牧区系统的适用性仍然不清楚。为了解决这一差距,我们评估了六种体积模型对桉树杂交品种Grancam 1277和Urograndis H-13树皮的总体积的估计能力。这些树木是在巴西圣保罗州Votuporanga市的一个Arenic Hapludult的一个农林牧区系统中种植的,该系统位于Cerrado生物群系内。对于Urograndis H-13,高田模型的F检验值最高(178.38),校正决定系数(0.93)值最高,标准误差最小(0.06),量化体积最准确。非线性和线性Schumacher-Hall模型对granam 1277最准确,该模型F检验最高(54.59),调整决定系数(0.8)值,标准误差最小(0.07)。
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Volumetric models for quantifying the wood volume of two eucalyptus hybrids in an agrosilvipastoral system
Quantifying the wood volume of forest stands is essential for planning sustainable forest management. The most efficient means of quantifying wood volume is volumetric equations. Many models for adjusting volumetric equations have been developed and tested, but their suitability for agrosilvipastoral systems remains unclear. To address this gap, we assessed the ability of six volumetric models to generate estimates of the total volume with bark of eucalyptus hybrids Grancam 1277 and Urograndis H-13. The trees were cultivated in an agrosilvipastoral system in an Arenic Hapludult in the municipality of Votuporanga, São Paulo State, Brazil, which is within the Cerrado biome. For Urograndis H-13, the Takata model was most accurate for quantifying volume, as it gave the highest F test (178.38) and adjusted coefficient of determination (0.93) values and the smallest standard error (0.06). The nonlinear and linear Schumacher-Hall models were most accurate for Grancam 1277, as this model gave the highest F test (54.59) and adjusted coefficient of determination (0.8) values and the smallest standard error (0.07).
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来源期刊
Australian Journal of Crop Science
Australian Journal of Crop Science 农林科学-农艺学
CiteScore
1.20
自引率
0.00%
发文量
75
审稿时长
3.5 months
期刊介绍: Information not localized
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